1. Field of the Invention
The present invention relates to and more particularly to edge extracting method and apparatus wherein edges of a fixed object and a moving object are detected from an image by a diffusion, delay, and differential neural network.
2. Description of the Prior Art
Recently, the visual processing abilities of animals have been studied applied and to image processors.
An eye of such an animal is constituted by neurons called ON units which exhibit an excitatory response to a spot excitation, and neurons called OFF units, which exhibit an inhibitory response to the spot excitation. By these ON units and OFF units, a neural response to the excitation appears in the form of superposed excitatory and inhibitory responses.
The regularity of such superposed excitatory and inhibitory responses is represented by two Gaussian functions G1 and G2. The neural response to the excitation is represented by a difference of boo Gaussian functions G1 and G2.
The Gaussian function G1 is 1/2.pi..sigma..sub.1.sup.2 exp (x.sup.2 /-2.sigma..sub.1.sup.2), whereas the Gaussian function G2 is 1/2.pi..sigma..sub.2.sup.2 exp (x.sup.2 /-2.sigma..sub.2.sup.2). The standard deviation .sigma..sub.2 of the Gaussian function G2 is slightly larger than the standard deviation .sigma..sub.1 of the Gaussian function G1.
The difference of the two Gaussian functions G1 and G2 can be expressed by a quadratic partial differential equation .gradient..sup.2 G of a Gaussian function satisfying the following equation: EQU .gradient..sup.2 G=1/2.pi..sigma..sub.1.sup.2 exp (x.sup.2 /-2.sigma..sub.1.sup.2)-1/2.pi..sigma..sub.2.sup.2 exp (x.sup.2 /-2.sigma..sub.2.sup.2).
wherein, .sigma..sub.1 &lt;.sigma..sub.2.
When the solution of the quadratic partial differential equation of the Gaussian function G is subjected to a Fourier's transform, the solution has a band pass filter characteristic,
Since the standard deviation .sigma. is inversely proportional to a frequency w, a frequency band is determined according to a filter characteristic given by a user. Depending on the frequency w, the standard deviation .sigma. is determined.
In other words, when an image signal I is received, a standard deviation .sigma. is determined based on a frequency band of the image signal to be filtered. As a result, the image signal I is filtered at the frequency band determined by the standard deviation. At this time, the image signal I is convolution-operated with a Gaussian function G having the standard deviation determined by the frequency band of the image signal I to be filtered. Based on the operation result, zero crossings can be found (i.e., where a value is changed from positive to negative or vice versa). These zero crossings are the edge of the image.
In the above operation procedure, the convolution operation of the image signal with the Gaussian function is executed for every frame. For each pixel, the multiplication processing amount is proportional to the dimension of the Gaussian function, namely, the mask size. The mask size is also proportional to the square of the standard deviation .sigma..
In other words, the mask size is 72.sigma..sup.2 in a .gradient..sup.2 G function. For an edge of an image signal having a frequency of 1, the standard deviation .sigma. should be .sigma.2. In this case, the mask consists of 144 pixels. Where an image signal is filtered at a low frequency band, the determined standard deviation .sigma. is increased because it is proportional to the frequency of the image signal to be filtered. On the other hand, the mask size become larger because it is proportional to the square of the standard deviation .sigma..
As mentioned above, when the mask size is large, a large amount of processing is required to perform the convolution of the input image signal with the Gaussian function. As a result, constructing a hardware implementation is very difficult. Since the standard deviation .sigma. determines only one frequency band, a plurality of filters are needed for filtering an image signal with a variety of frequency characteristics. The software solution is also very difficult to implement because of the very slow processing speed. As a result, it is impossible to accomplish a real time processing of image signals.
There may also exist a situation requiring only the extraction of moving objects from a number of objects present in an image or the of tracks of the moving objects.
Conventional devices for sensing moving objects include a radar having a transmission function and an image processing device using a computer. In the case of the radar, the extraction of a moving object is achieved by detecting a difference between a frequency reflected from the moving object and a transmission frequency by utilizing a doppler effect that a frequency reflected from a moving object has a difference from a transmission frequency by a frequency proportional to the motion speed of the moving object. Since the waves reflected from the moving object are frequently varied in phase, this radar requires the use of a canceler for removing the reflected waves. For detecting frequencies with a small quantity of reflected waves, an expensive transmitter and receiver generating few errors should be used.
In the device for detecting a moving object using an image processing system having no transmission function, a canceler having a memory unit controlled by software issued. Good processing results can be obtained in so far as the canceler utilizes past information as much as possible, To this end, large memory capacity is required.